RNN Models

Once we have an implementation of the Layer Class https://github.com/arrayfire/arrayfire_ml/issues/17 , the Optimizer class and the DataSet class we can go about creating RNN flavors. There are 3 models that should be implemented:
- [ ] Vanilla RNN
- [ ] LSTM
- [ ] GRU

These will require the implementation of their derivatives and their forward prop values.
Certain details to consider:
- RNN's have a stack of weight matrices and bias' (not just 1 per Layer, thus the Layer needs to be general enough to handle this)
- The optimization needs to be handled via two methods:
- [ ] RTRL (real time recurrent learning) &
- [ ] BPTT (backprop through time)

To enable the above two methods of learning we should consider inheriting from Layer and implementing a Recurrent Layer.